Product Engineering Lead (QVAC) (100% remote)

Tether Operations Limited

20h•Remote

About The Position

QV.AC is a general-purpose AI assistant that runs locally on users’ devices, giving people control over their data and AI use without cloud dependency or data harvesting. It focuses on local, on-device, privacy-preserving AI connected across Tether’s peer-to-peer ecosystem. The Product Engineering Lead turns product direction into reliable, user-loved features for QV.AC, leading the teams responsible for its features and app experience on top of the local AI stack. The role is approximately 70% engineering leadership and 30% product partnership. Product owns what is built and why; this role owns how it is built and the engineering standard. The position reports directly to the CTO and takes direction from product.

Requirements

Experience managing engineers or engineering leads who ship consumer products, ideally AI-powered applications.
Strong product sense and the ability to distinguish valuable features from unnecessary complexity.
Intuition for applied AI and on-device constraints, including model size, latency, memory, battery, and the difference between a successful demo and a reliable feature.
Heavy use of AI tools in personal work and enthusiasm for experimenting with new models and capabilities.
Deep JavaScript and React expertise.
Initiative, curiosity, resilience, and the ability to give and receive direct critique.

Nice To Haves

Experience with local or on-device inference.
Experience with LLM evaluations.
Experience with cross-platform mobile and desktop applications.
Open-source experience.

Responsibilities

Run the app and feature teams day to day by unblocking work, reviewing contributions, and keeping teams shipping.
Set and maintain high engineering standards for performance, reliability, and clean APIs.
Own the technical design of features and the API surface between the AI core and the app.
Ensure close collaboration with AI research and deep tech teams on code reviews, design, and work involving models or inference.
Partner with product managers to turn vision and roadmap plans into scoped, buildable work.
Challenge mismatches between scope and engineering reality and propose better approaches where appropriate.
Bring an engineering perspective to product decisions, particularly regarding current capabilities and limitations of local models.
Help teams move quickly by using AI tools and rapid prototypes to validate ideas before committing to them.
Clarify team goals and ensure everyone understands what success looks like.
Mentor team leads in building effective team operating systems, improving cross-team collaboration, and developing as leaders.
Lead by example, contributing directly when needed and stepping back as teams become ready to take ownership.